Recently, a technology called deep learning developed in various fields. Particularly, a technology called a convolutional neural network (CNN), which is a kind of deep learning, has attracted attention in the field of object recognition. CNN is a model that simulates a person's brain function based on an assumption that, when a person recognizes an object, basic features of the object are extracted, a complicated calculation is performed inside the brain, and then the object is recognized based on a result of the calculation. In general, the CNN may use various filters for extracting a feature of an image through a convolution operation, a non-linear activation function (e.g., a sigmod function, a rectified linear unit (ReLU) function, etc.) or pooling for adding a non-linear characteristic, and the like.
Interest in deep learning has been increasing in the fields of various medical devices (e.g., ultrasound waves, computed tomography (CT), magnetic resonance imaging (MRI), etc.). For example, there is an increasing interest in analyzing a medical image (e.g., lesion search (or detect), feature extraction, boundary extraction, classification, etc.) by applying deep learning to a computer aided diagnosis (CAD) device.